Variance component testing in semiparametric mixed models

DOI10.1016/j.jmva.2004.04.012zbMath1051.62021OpenAlexW2061808048MaRDI QIDQ1882937

Wing-Kam Fung, Zhong-yi Zhu

Publication date: 1 October 2004

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2004.04.012

zbMATH Keywords

SimulationsSmoothing splineHeteroscedasticityScore testRandom effectsSemiparametric mixed models

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Parametric hypothesis testing (62F03) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (10)

\(U\)-tests for variance components in linear mixed models ⋮ Bayesian variable selection for mixed effects model with shrinkage prior ⋮ Testing the correlation and heterogeneity for hierarchical nonlinear mixed-effects models ⋮ Inference for mixed models of ANOVA type with high-dimensional data ⋮ Bayesian nonparametric centered random effects models with variable selection ⋮ Testing for random effects in linear mixed models for longitudinal data under moment conditions ⋮ A residual-based test for variance components in linear mixed models ⋮ Improved \(U\)-tests for variance components in one-way random effects models ⋮ Testing for Random Effects in Growth Curve Models ⋮ Small area estimation via heteroscedastic nested-error regression

Cites Work

• Diagnostics for heteroscedasticity in regression
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• Tests of Homogeneity for Generalized Linear Models
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• Miscellanea. Score tests for heteroscedasticity in wavelet regression
• Semiparametric Stochastic Mixed Models for Longitudinal Data
• Inference in Generalized Additive Mixed Models by Using Smoothing Splines
• Variance component testing in generalised linear models with random effects
• Diagnosing Explainable Heterogeneity of Variance in Random-Effects Models
• Some remarks on overdispersion
• Use of Modified Profile Likelihood for Improved Tests of Constancy of Variance in Regression
• Testing and Adjusting for Departures from Nominal Dispersion in Generalized Linear Models
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